Bayesian statistical methods for genetic association studies (original) (raw)

References

1. Sellke, T., Bayarri, M. J. & Berger, J. O. Calibration of p values for testing precise null hypotheses. Am. Stat. 55, 62–71 (2001).
   Article Google Scholar
2. Sterne, J. A. C. & Davey Smith, G. Sifting the evidence — what's wrong with significance tests? BMJ 322, 226–231 (2001).
   Article CAS PubMed PubMed Central Google Scholar
3. Ioannidis, J. P. A. Effect of formal statistical significance on the credibility of observational associations. Am. J. Epidem. 168, 374–383 (2008).
   Article Google Scholar
4. Ayres, K. L. & Balding, D. J. Measuring departures from Hardy–Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient. Heredity 80, 769–777 (1998).
   Article PubMed Google Scholar
5. Shoemaker, J. S., Painter, I. S. & Weir, B. S. Bayesian statistics in genetics — a guide for the uninitiated. Trends Genet. 15, 354–358 (1999).
   Article CAS PubMed Google Scholar
6. Beaumont, M. A. & Rannala, B. The Bayesian revolution in genetics. Nature Rev. Genet. 5, 251–261 (2004).
   Article CAS PubMed Google Scholar
7. Marjoram, P. & Tavare, S. Modern computational approaches for analysing molecular genetic variation data. Nature Rev. Genet. 7, 759–770 (2006).
   Article CAS PubMed Google Scholar
8. O'Hara, R. B., Cano, J. M., Ovaskainen, O., Teplitsky, C. & Alho, J. S. Bayesian approaches in evolutionary quantitative genetics. J. Evol. Biol. 21, 949–957 (2008).
   Article CAS PubMed Google Scholar
9. Wakefield, J. Bayesian methods for examining Hardy–Weinberg equilibrium. Biometrics 13 May 2009 (doi:10.1111/j.1541-0420.2009.01267.x).
   Article PubMed PubMed Central Google Scholar
10. Lunn, D. J., Whittaker, J. C. & Best, N. A Bayesian toolkit for genetic association studies. Genet. Epidem. 30, 231–247 (2006).
    Article Google Scholar
11. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genet. 39, 906–913 (2007). The supplementary material of this article includes a review of frequentist tests and BFs for single-SNP association and a brief review of the Laplace approximation. In particular, it describes the Bayesian analysis methods implemented in the SNPTEST software.
    Article CAS PubMed Google Scholar
12. Servin, B. & Stephens, M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet. 3, e114 (2007). This paper includes a description of several of the Bayesian analysis methods that are implemented in the BIMBAM software, including the Bayesian multi-SNP analysis methods that we used in this Review.
    Article PubMed PubMed Central Google Scholar
13. The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007). A landmark paper because of the size of the studies, the pioneering use of unphenotyped common controls for a range of diseases and the large number of novel genetic associations reported. The authors also advocate the use of Bayesian approaches for evaluating evidence of association, which was reported alongside traditional p -values for the first time in a major study.
14. Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).
    Article CAS PubMed PubMed Central Google Scholar
15. Hosking, F. J., Sterne, J. A. C., Smith, G. D. & Green, P. J. Inference from genome-wide association studies using a novel Markov model. Genet. Epidem. 32, 497–504 (2008).
    Article Google Scholar
16. Verzilli, C. et al. Bayesian meta-analysis of genetic association studies with different sets of markers. Am. J. Hum. Genet. 82, 859–872 (2008).
    Article CAS PubMed PubMed Central Google Scholar
17. Fridley, B. L. Bayesian variable and model selection methods for genetic association studies. Genet. Epidem. 33, 27–37 (2009).
    Article Google Scholar
18. Newcombe, P. J. et al. Multilocus Bayesian meta-analysis of gene–disease associations. Am. J. Hum. Genet. 84, 567–580 (2009).
    Article CAS PubMed PubMed Central Google Scholar
19. Wakefield, J. Reporting and interpretation in genome-wide association studies. Intern. J. Epidem. 37, 641–653 (2008).
    Article Google Scholar
20. Guan, Y. & Stephens, M. Practical issues in imputation-based association mapping. PLoS Genet. 4, e1000279 (2008). This article includes a detailed discussion of the advantages of Bayesian methods over frequentist methods when assessing associations with imputed SNPs.
    Article PubMed PubMed Central Google Scholar
21. Balding, D. J. A tutorial on statistical methods for population association studies. Nature Rev. Genet. 7, 781–791 (2006). This Review covers: preliminary analyses (of Hardy–Weinberg and linkage equilibria, inference of phase and missing genotypes); single-SNP tests of association for binary, continuous and ordinal outcomes; multi-SNP and haplotype analyses; and dealing with population stratification and multiple-testing issues, largely within the frequentist framework.
    Article CAS PubMed Google Scholar
22. Jeffreys, H. Theory of Probability (Oxford Univ. Press, 1961).
    Google Scholar
23. Good, I. J. The Bayes/non-Bayes compromise: a brief review. J. Am. Stat. Assoc. 87, 597–606 (1992).
    Article Google Scholar
24. Seaman, S. R. & Richardson, S. Equivalence of prospective and retrospective models in the Bayesian analysis of case–control studies, Biometrika 91, 15–25 (2004).
    Article Google Scholar
25. Freidlin, B., Zheng, G., Li, Z. H. & Gastwirth, J. L. Trend tests for case–control studies of genetic markers: power, sample size and robustness. Hum. Hered. 53, 146–152 (2002).
    Article CAS PubMed Google Scholar
26. The SEARCH Collaborative Group. SLCO1B1 variants and statin-induced myopathy — a genomewide study. N. Engl. J. Med. 359, 789–799 (2008).
27. Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2009).
    Article Google Scholar
28. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996).
    Google Scholar
29. Hoggart, C. J., Whittaker, J. C., De Iorio, M. & Balding, D. J. Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet. 4, e1000130 (2008).
    Article PubMed PubMed Central Google Scholar
30. Kavvoura, F. K. & Ioannidis, J. P. A. Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls. Hum. Genet. 123, 1–14 (2008).
    Article PubMed Google Scholar
31. Van Houwelingen, H. & Lebrec, J. P. in Meta-analysis and Combining Information in Genetics and Genomics (eds Guerra, R. et al.) 49–66 (CRC Press, 2009).
    Google Scholar
32. Ioannidis, J. P., Patsopoulos, N. A. & Evangelou, E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS ONE 2, e841 (2007).
    Article PubMed PubMed Central Google Scholar
33. Lunn, D. J., Thomas, A., Best, N. & Spiegelhalter, D. WinBUGS — a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10, 325–337 (2000).
    Article Google Scholar
34. Thompson, J. R., Minelli, C., Abrams, K. R., Thakkinstian, A. & Attia, J. Combining information from related meta-analyses of genetic association studies. J. R. Stat. Soc. C 57, 103–115 (2008).
    Article Google Scholar
35. Hoggart, C. J., Clark, T. G., De Iorio, M., Whittaker, J. C. & Balding, D. J. Genome-wide significance for dense SNP and resequencing data. Genet. Epidem. 32, 179–185 (2008).
    Article Google Scholar
36. Veyrieras, J.-B. et al. High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet. 4, e1000214 (2008).
    Article PubMed PubMed Central Google Scholar
37. Lee, S.-I. et al. Learning a prior on regulatory potential from eQTL data. PLoS Genet. 5, e1000358 (2009).
    Article PubMed PubMed Central Google Scholar
38. Chen, R. et al. FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease. Genome Biol. 9, R170 (2008).
    Article PubMed PubMed Central Google Scholar
39. Tachmazidou, I., Andrew, T., Verzilli, C. J., Johnson, M. R. & De Iorio, M. Bayesian survival analysis in genetic association studies. Bioinformatics 24, 2030–2036 (2008).
    Article CAS PubMed PubMed Central Google Scholar
40. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate — a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
    Google Scholar
41. Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. B 64, 479–498 (2002).
    Article Google Scholar
42. Wakefield, J. Bayes factors for genome-wide association studies: comparison with _P_-values. Genet. Epidem. 33, 79–86 (2009). This is the last in a sequence of three single-author papers published by Wakefield in successive years. This paper uses the approximate BF introduced in Reference 14 to highlight what can be regarded as implicit assumptions in the use of standard p -values as the primary summaries of evidence for association.
    Article Google Scholar
43. Wang, W. Y. S., Barratt, B. J., Clayton, D. G. & Todd, J. A. Genome-wide association studies: theoretical and practical concerns. Nature Rev. Genet. 6, 109–118 (2005).
    Article CAS PubMed Google Scholar
44. Gorlov, I. P., Gorlova, O. Y., Sunyaev, S. R., Spitz, M. R. & Amos, C. I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82, 100–112 (2008).
    Article CAS PubMed PubMed Central Google Scholar
45. Greenland, S. Multiple comparisons and association selection in general epidemiology. Intern. J. Epidem. 37, 430–434 (2008).
    Article Google Scholar
46. Scheipl, F. & Kneib, T. Locally adaptive Bayesian P-splines with a normal-exponential-gamma prior. Comput. Stat. Data Anal. 53, 3533–3552 (2009).
    Article Google Scholar
47. Reiner, A. P. et al. Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1α are associated with C-reactive protein. Am. J. Hum. Genet. 82, 1193–1201 (2008).
    Article CAS PubMed PubMed Central Google Scholar

Download references